CANalyzer: An Interactive Dashboard to Log CAN Messages and Analyze Data; The Future of Work: Analyzing Labor Market Evolution in the Age of Artificial Intelligence

My technical and STS research projects both explore the rapidly evolving landscape of artificial intelligence (AI) from different but complementary angles. While my technical work focuses on the engineering principles of bringing new technologies into the workforce because of technological breakthroughs, my STS research analyzes the broader societal implications of AI on the job market, utilizing Technological Momentum as its lens. My motivation for pursuing these projects is rooted in a desire to build AI-driven systems as well as reflect on how said systems impact company hiring practices, disrupt employment patterns, and reshape policy. I am particularly interested in how AI, while promising increased efficiency and innovation, also raises important questions about job stability, equity, and long term economic stability. The connection between these two concepts helps me understand that technological development, once shaped by social influences, gradually builds momentum of its own and becomes a force that reshapes society in ways that are becoming increasingly difficult to reverse or redirect. This realization has underscored the importance of developing technologies not just for sake of technical excellence but also with ethical intentions and social responsibility.
As embedded systems become increasingly complex, such as those used for automobile applications like a Solar Car, managing communication through Controller Area Network (CAN) messages between its components has become more challenging, highlighting the need for improved data management and debugging solutions. To address this, we developed a dashboard to simplify the process of CAN message management and provide real-time, human-readable updates from components of the car. This dashboard, termed the CANaylzer, leverages a python backend with an SQLite database and Flask application to organize filtered CAN messages by board and display said messages on a dynamically contained interface using html and JavaScript. Furthermore, we added the ability for live data to be visualized using built-in graphical analysis, enabling efficient and simplistic real-time monitoring of component performance. Early testing has verified system performance and has been used to optimize power distribution and motor usage over time. Moreover, graphical analysis has been helpful in catching component bugs, which further improve the efficiency of the car. Future development of the CANaylzer aims to provide more debugging options such as data overlay and provide more filtering options to streamline the process of data management for complex embedded systems like a Solar Car.
Artificial intelligence (AI) is transforming industries at an unprecedented pace, fundamentally reshaping workforce structures and employment dynamics. This paper explores the historical development of AI, its integration into various sectors, and its implications for labor markets through the lens of technological momentum. As AI advances, automation has led to job displacement in certain fields while simultaneously generating new employment opportunities that require specialized skills. This study examines how AI adoption has influenced labor demand, analyzing trends across industries such as manufacturing, healthcare, finance, and education. Using a historical perspective, this research identifies patterns of workforce adaptation to technological shifts, comparing AI-driven changes to past industrial revolutions like the dotcom boom. While concerns about widespread job losses persist, AI’s role in augmenting human capabilities, improving productivity, and fostering new career paths cannot be overlooked. However, the transition necessitates proactive measures, including reskilling programs, policy interventions, and ethical considerations to ensure an equitable distribution of AI’s benefits. By applying the technological momentum framework, this study highlights the interplay between social, economic, and technological factors that shape the future of work. Understanding these dynamics is essential for developing strategies that mitigate workforce disruptions while maximizing AI’s potential for economic growth. Ultimately, this research provides insights into how businesses, policymakers, and workers can navigate AI’s impact on employment, fostering a more resilient and adaptable workforce in an era of rapid technological change.
Working on both the technical and STS in parallel provided a comprehensive perspective on the dual nature of innovation in how we build technologies and how those technologies, in turn, build us. My technical work sharpened my programming skills and system design, while my STS research deepened my understanding of the ethical, social, and economic contexts in which these technologies operate. Integrating both projects gave me the opportunity to consider not just how to develop systems, but also how to implement them responsibly, with attention to their long-term impact on society. Through this process, I developed a more holistic approach to engineering that balances efficiency and progress with equity and foresight.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rosanne Vrugtman

STS Advisor: Bryn Seabrook

Technical Team Members: Ethan Ermovick, An Huynh